546 research outputs found
A Framework for Automatic Behavior Generation in Multi-Function Swarms
Multi-function swarms are swarms that solve multiple tasks at once. For
example, a quadcopter swarm could be tasked with exploring an area of interest
while simultaneously functioning as ad-hoc relays. With this type of
multi-function comes the challenge of handling potentially conflicting
requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites
in combination with a suitable controller structure, a framework for automatic
behavior generation in multi-function swarms is proposed. The framework is
tested on a scenario with three simultaneous tasks: exploration, communication
network creation and geolocation of RF emitters. A repertoire is evolved,
consisting of a wide range of controllers, or behavior primitives, with
different characteristics and trade-offs in the different tasks. This
repertoire would enable the swarm to transition between behavior trade-offs
online, according to the situational requirements. Furthermore, the effect of
noise on the behavior characteristics in MAP-elites is investigated. A moderate
number of re-evaluations is found to increase the robustness while keeping the
computational requirements relatively low. A few selected controllers are
examined, and the dynamics of transitioning between these controllers are
explored. Finally, the study develops a methodology for analyzing the makeup of
the resulting controllers. This is done through a parameter variation study
where the importance of individual inputs to the swarm controllers is assessed
and analyzed
A Framework for Automatic Behavior Generation in Multi-Function Swarms
17 USC 105 interim-entered record; under review.Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of Radio Frequency (RF) emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire enables the swarm to online transition between behaviors featuring different trade-offs of applications depending on the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study investigates the importance of individual sensor or controller inputs. This is done through ablation, where individual inputs are disabled and their impact on the performance of the swarm controllers is assessed and analyzed
An approach to evolve and exploit repertoires of general robot behaviours
Recent works in evolutionary robotics have shown the viability of evolution driven by behavioural novelty and diversity. These evolutionary approaches have been successfully used to generate repertoires of diverse and high-quality behaviours, instead of driving evolution towards a single, task-specific solution. Having repertoires of behaviours can enable new forms of robotic control, in which high-level controllers continually decide which behaviour to execute. To date, however, only the use of repertoires of open-loop locomotion primitives has been studied. We propose EvoRBC-II, an approach that enables the evolution of repertoires composed of general closed-loop behaviours, that can respond to the robot's sensory inputs. The evolved repertoire is then used as a basis to evolve a transparent higher-level controller that decides when and which behaviours of the repertoire to execute. Relying on experiments in a simulated domain, we show that the evolved repertoires are composed of highly diverse and useful behaviours. The same repertoire contains sufficiently diverse behaviours to solve a wide range of tasks, and the EvoRBC-II approach can yield a performance that is comparable to the standard tabula-rasa evolution. EvoRBC-II enables automatic generation of hierarchical control through a two-step evolutionary process, thus opening doors for the further exploration of the advantages that can be brought by hierarchical control.info:eu-repo/semantics/acceptedVersio
QED: using Quality-Environment-Diversity to evolve resilient robot swarms
In swarm robotics, any of the robots in a swarm may be affected by different
faults, resulting in significant performance declines. To allow fault recovery
from randomly injected faults to different robots in a swarm, a model-free
approach may be preferable due to the accumulation of faults in models and the
difficulty to predict the behaviour of neighbouring robots. One model-free
approach to fault recovery involves two phases: during simulation, a
quality-diversity algorithm evolves a behaviourally diverse archive of
controllers; during the target application, a search for the best controller is
initiated after fault injection. In quality-diversity algorithms, the choice of
the behavioural descriptor is a key design choice that determines the quality
of the evolved archives, and therefore the fault recovery performance. Although
the environment is an important determinant of behaviour, the impact of
environmental diversity is often ignored in the choice of a suitable
behavioural descriptor. This study compares different behavioural descriptors,
including two generic descriptors that work on a wide range of tasks, one
hand-coded descriptor which fits the domain of interest, and one novel type of
descriptor based on environmental diversity, which we call
Quality-Environment-Diversity (QED). Results demonstrate that the
above-mentioned model-free approach to fault recovery is feasible in the
context of swarm robotics, reducing the fault impact by a factor 2-3. Further,
the environmental diversity obtained with QED yields a unique behavioural
diversity profile that allows it to recover from high-impact faults
A Quality-Diversity Approach to Evolving a Repertoire of Diverse Behaviour-Trees in Robot Swarms
Designing controllers for a swarm of robots such that collaborative
behaviour emerges at the swarm level is known to be challenging.
Evolutionary approaches have proved promising, with attention turning
more recently to evolving repertoires of diverse behaviours that can
be used to compose heterogeneous swarms or mitigate against faults.
Here we extend existing work by combining a Quality-Diversity algorithm
(MAP-Elites) with a Genetic-Programming (GP) algorithm to
evolve repertoires of behaviour-trees that define the robot controllers.
We compare this approach with two variants of GP, one of which uses
an implicit diversity method. Our results show that the QD approach results
in larger and more diverse repertoires than the other methods with
no loss in quality with respect to the best solutions found. Given that
behaviour-trees have the added advantage of being human-readable compared
to neural controllers that are typically evolved, the results provide
a solid platform for future work in composing heterogeneous swarms
Evolution of Neuro-Controllers for Robots' Alignment using Local Communication
info:eu-repo/semantics/publishe
Evolutionary swarm robotics: a theoretical and methodological itinerary from individual neuro-controllers to collective behaviours
In the last decade, swarm robotics gathered much attention in the research community. By drawing inspiration from social insects and other self-organizing systems, it focuses on large robot groups featuring distributed control, adaptation, high robustness, and flexibility. Various reasons lay behind this interest in similar multi-robot systems. Above all, inspiration comes from the observation of social activities, which are based on concepts like division of labor, cooperation, and communication. If societies are organized in such a way in order to be more efficient, then robotic groups also could benefit from similar paradigms
Evolution of Neuro-Controllers for Robots\u27 Alignment using Local Communication
In this paper, we use artificial evolution to design homogeneous neural network controller for groups of robots required to align. Aligning refers to the process by which the robots managed to head towards a common arbitrary and autonomously chosen direction starting from initial randomly chosen orientations. The cooperative interactions among robots require local communications that are physically implemented using infrared signalling. We study the performance of the evolved controllers, both in simulation and in reality for different group sizes. In addition, we analyze the most successful communication strategy developed using artificial evolution
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